基于功能磁共振影像的脑功能网络组织特征研究

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	基于功能磁共振影像的脑功能网络组织特征研究
	初从颖
	2016-05
学位类型	工学博士
中文摘要	脑是人体最复杂、最高效的信息处理系统。空间上离散分布但功能上相互作用的脑区构成了脑功能网络，它们协同合作、相互传递信息使得人们可以完成复杂的行为和高级的认知功能。研究人脑的功能组织规律是我们认识脑，了解脑的重要一步。基于大脑所处的不同工作状态，本文从任务状态和静息状态两方面出发，以脑功能网络为切入点，研究人脑的功能共激活网络和功能组织特性。主要的工作如下：基于任务状态下的功能影像数据，提出了挖掘人脑的功能共激活网络的新方法。随着功能神经影像技术的不断发展，其促进了在不同认知任务下关于脑激活反应的研究。基于激活点坐标的荟萃分析已经越来越多的被用来挖据实验中具有一致性激活的脑区。但是，同一实验中的共激活关系，其反映了脑区间潜在的功能联系，并没有得到广泛的研究。特别地，能够反映详细的共激活关系网络的体素级别的共激活关系仍然需要被构建。为了估计体素级别的共激活模式以及进一步发现共激活关系的网络结构，我们提出了共激活可能性估计的方法（Co-activation Probability Estimation，CoPE）来定义和构建实验间保持显著一致的共激活关系。通过采用改进的置换检验方法，我们对共激活关系的显著性进行了检验。在检验得到的共激活关系中，基于具有共激活关系的体素之间的距离，共激活关系可以自然的被区分为短程共激活和长程共激活。其中，短程共激活反映的是该脑区在实验间具有一致的激活，长程共激活反映的是一致的共激活关系，其吸引更多的研究兴趣。CoPE方法通过五组测试数据和一组真实的激活数据来对方法的鲁棒性，可用性进行验证。在测试数据上，CoPE方法能找到与设计相符的短程共激活，以及长程共激活关系。在真实数据上（工作记忆的激活数据），CoPE方法发现了与工作记忆相关的左偏的工作记忆核心共激活网络。基于静息状态的功能磁共振数据，提出了FunCo指标，定量地度量人脑的功能复杂性。人脑静息状态下蕴含的自发活动已经被证明可以反映整个系统内在的组织结构，例如，脑功能网络的结构。在宏观尺度上，网络观点的兴起已经开辟的一条新的道路来探索人脑的功能组织特性。此处，我们基于网络的观点，在健康被试的数据上，研究了脑的功能复杂性分布模式。我们提出脑区的功能复杂性与该脑区参与到不同的功能网络的模式是相关的。利用改进的ICA的模型，我们对每个个体的静息态功能磁共振数据进行了有参考的独立成分分解。分解得到的独立成分对应着静息状态下的若干内在连接网络的模式。基于每个体素参与到不同内在连接网络的模式，我们定义了功能复杂性指标（functioanl diversity，FunCo）来定量的描述体素级的参与到不同内在连接网络的参与模式。我们发现FunCo指标在全脑上的分布是有着强弱的变化的。例如，初级运动皮层表现出低的FunCo值的分布，而楔前叶就表现出来显著高的FunCo值的分布。FunCo指标揭示了脑区的不同功能角色，而这些功能角色是通过对内在连接网络的参与模式得到了体现。更进一步，我们关注了大脑左右半球之间的FunCo指标的差异区域的分布。其中我们发现与语言功能相关的脑区（布洛卡区等）在左半球表现了低功能复杂性，相较于右半球而言。而，跟顶下小叶等脑区在右半球表现了相较于左半球对应脑区低的功能复杂性。在将来的实际应用中，FunCo指标可以作为定量度量大脑功能复杂性变化的指标而应用到与脑功能失常的脑疾病研究中去。在静息状态下，提出了潜在激活模式的假说，探寻人脑自发活动的动态性变化背后可能的机理。人脑功能网络间的连接并不是一成不变的，其是在时间尺度上动态变化的。这种动态变化可以通过静息态功能影像数据得到反映。目前，如何描述人脑动态变化背后的原因一直是一个待解决的问题。在当前研究中，我们提出一种可能的假设，即，人脑的动态性活动与人脑中的潜在激活模式相关。我们进一步的假设这种潜在激活模式在大脑处于不同状态时，是稀疏表达的。针对前述的假设，我们采用了字典学习的框架对该假设进行建模。通过在线字典学习的方法，对模型进行了解算，得到了潜在激活模式以及其对应的稀疏表达的参数。我们发现了几种频繁参与到脑活动中的潜在激活模式，其与默认网络，突显网络以及额顶网络具有联系。这种潜在激活模式在时间尺度上的稀疏表达，可能会为解释人脑自发活动的动态变化提供一种新的选择。
英文摘要	Human brain is the most complicated and efficient information processing system. Regions that are apart in distance interacting in function form a particular functional network. The coordination of functional networks is the basic property of human brain, for performing complex behavior and cognitive funtions. Studying the functional organization of human brain is an important step for us to advance our understanding about brain. Based on the different state of brain (task state / resting state), we studied the functional property of human brain and the co-activation network structure. These studies included the following parts: Based on the task-based functional neuroimaging, we studied the functional co-activation network. Recent progress in functional neuroimaging has prompted studies of brain activation during various cognitive tasks. Coordinate-based meta-analysis has been utilized to discover the brain regions that are consistently activated across experiments. However, within-experiment co-activation relationships, which can reflect the underlying functional relationships between different brain regions, have not been widely studied. In particular, voxel-wise co-activation, which may be able to provide a detailed configuration of the co-activation network, still needs to be modeled. To estimate the voxel-wise co-activation pattern and deduce the co-activation network, a Co-activation Probability Estimation (CoPE) method was proposed to model within-experiment activations for the purpose of defining the co-activations. A permutation test was adopted as a significance test. Moreover, the co-activations were automatically separated into local and long-range ones, based on distance. The two types of co-activations describe distinct features: the first reflects convergent activations; the second represents co-activations between different brain regions. The validation of CoPE was based on five simulation tests and one real dataset derived from studies of working memory. Both the simulated and the real data demonstrated that CoPE was not only able to find local convergence but also significant long-range co-activation. In particular, CoPE was able to identify a ‘core’ co-activation network in the working memory dataset. As a data-driven method, the CoPE method can be used to mine underlying co-activation relationships across experiments in future studies. Based on the resting-state functional MRI, we quantitatively estimated the functional complexity of human brain. Spontaneous fluctuations underlying the brain activity can reflect the intrinsic organization of the system, such as the functional brain networks. In large scale, a network perspective has emerged as a new avenue to explore the functional properties of human brain. Here, we studied functional diversity in healthy subjects based on the network perspective. We hypothesized that the patterns of participation of different functional networks were related with the functional diversity of particular brain regions. Independent component analysis (ICA) was adopted to detect the intrinsic connectivity networks (ICNs) based on the data of resting-state functional MRI. An index of functional complexity (FunCo index) was proposed to quantitatively describe the degree of anisotropic distribution related with participation of various ICNs. We found that FunCo index continuously varied across the brain, for example, the primary motor cortex with low FunCo value and the precuneus with significantly high FunCo value. The FunCo values indicated the different functional roles of the corresponding brain regions, which were reflected by the various patterns of participation of ICNs. Further, we studied functional lateralization of human brain using FunCo index. We found that the Broca area related with the function of language shown significantly low FunCo value, compared with the right part. Meanwhile, the partial part of inferior partial lobule possessed low FunCo value in right side, which was related with the function of visuospatial attention. The FunCo index can be used as a new approach to quantitatively characterize the functional diversity of human brain, even for the changed functional properties caused by the psychiatric disorders. Based on the resting-state fMRI, we also tried to find the factors related with the functional dynamic of human brain. The dynamic organization of human brain functional networks can be revealed through resting-state fMRI. It is still an open question about how to determine the essential factors underlying the dynamic activity of human brain. In this study, we proposed the assumption that the dynamic activity of brain was companied with various involvements of latent active patterns (LAPs). We further supposed that LAPs were sparsely involved with different brain states. We modeled the assumptions by adopting a dictionary-learning framework. An online dictionary learning method was used to calculate the LAPs and the sparse loading parameters. Based on the results obtained from the resting-state fMRI dataset, we found some commonly represented LAPs that were involved with the default mode network, salience network and frontoparietal attention network. The sparse represented LAPs at each time point were related with the time-varying activity. LAPs provided a new viewpoint to mine the factors related with the dynamic organization of brain activity.
关键词	功能磁共振脑功能网络共激活功能复杂性动态性潜在激活模式
文献类型	学位论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11962
专题	毕业生_博士学位论文
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	初从颖. 基于功能磁共振影像的脑功能网络组织特征研究[D]. 北京. 中国科学院研究生院,2016.

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